PINT: Probabilistic In-band Network Telemetry (2007.03731v1)

Abstract: Commodity network devices support adding in-band telemetry measurements into data packets, enabling a wide range of applications, including network troubleshooting, congestion control, and path tracing. However, including such information on packets adds significant overhead that impacts both flow completion times and application-level performance. We introduce PINT, an in-band telemetry framework that bounds the amount of information added to each packet. PINT encodes the requested data on multiple packets, allowing per-packet overhead limits that can be as low as one bit. We analyze PINT and prove performance bounds, including cases when multiple queries are running simultaneously. PINT is implemented in P4 and can be deployed on network devices. Using real topologies and traffic characteristics, we show that PINT concurrently enables applications such as congestion control, path tracing, and computing tail latencies, using only sixteen bits per packet, with performance comparable to the state of the art.

• The paper introduces a probabilistic approach to distribute telemetry data, reducing per-packet overhead to as little as 16 bits while retaining network visibility.
• It leverages a P4-based implementation and a global hashing mechanism to coordinate telemetry collection implicitly across network packets.
• Evaluations on real network topologies for path tracing, congestion control, and latency estimation demonstrate PINT's effectiveness in large-scale deployments.

Probabilistic In-band Network Telemetry: A Summary of PINT

The paper introduces Probabilistic In-band Network Telemetry (PINT), a novel approach to network telemetry that aims to address the inherent overhead limitations of the existing In-band Network Telemetry (INT). INT provides network visibility by embedding packet-level telemetry information into data packets as they traverse the network. While this mechanism allows for insightful data collection crucial for effective network management, it also introduces a considerable per-packet overhead. This overhead grows linearly with the number of hops in a packet's path and the amount of telemetry data collected per switch, posing performance drawbacks such as increased flow completion times and reduced application-level goodput.

PINT proposes a probabilistic framework where the added telemetry information is spread probabilistically across multiple packets in a flow, thereby limiting the per-packet overhead. It employs approximation techniques to encode only essential information, fulfilling the requirements of telemetry applications while drastically mitigating overhead. Notably, PINT supports an overhead budget that could be as low as a single bit per packet, thereby making it suitable for environments with strict resource constraints.

Key Contributions

1. Framework and Architecture: PINT redefines how telemetry data is handled by distributing it probabilistically across packets rather than fully in each. This includes devising a query engine that coordinates through a global hashing mechanism, facilitating implicit coordination without incurring additional packet overhead.
2. Analysis and Implementation: The paper offers rigorous analytical performance bounds and demonstrates the efficacy of PINT through a P4-based implementation. It successfully integrates into existing programmable network devices and leverages the limited capabilities of such hardware to efficiently manage telemetry tasks.
3. Evaluation: Leveraging real network topologies, PINT is evaluated on various use cases, namely path tracing, congestion control, and tail latency estimation. The paper reports that PINT achieves performance levels comparable to INT with only sixteen bits per packet, significantly reducing packet overhead.

Implications and Future Prospects

PINT offers practical improvements for telemetry applications, addressing the overhead concerns by allowing similar visibility into network conditions while reducing the demand on bandwidth. For theoretical implications, this opens venues in network telemetry research, focusing on reducing overhead through probabilistic methods and approximations.

From a practical standpoint, PINT's design has profound implications for network operators, particularly those managing large-scale data centers and ISP networks where packet overhead plays a critical role in performance. By providing a method to adjust telemetry resolutions and aggregate information flexibly, PINT can adaptively suit the demands of different applications while keeping network utilization in check.

Furthermore, the paper hints at the future direction of probabilistic methods in managing network telemetry, potentially inspiring subsequent research and development initiatives. It invites further exploration into extending these methods beyond telemetry, perhaps towards areas like network security or adaptive routing, where overhead constraints play similarly critical roles.

Conclusion

PINT represents a concerted effort to mitigate the balance between network visibility and operational efficiency. By innovating how telemetry data is probabilistically handled, PINT sets a new precedent for intelligent overhead management in network telemetry. Future work can build upon this framework to refine the methodologies further or explore new applications within this efficient encoding and aggregation paradigm.